Tracking Spatial and Temporal Variation in Forest Canopy Conditions in the Boreal Forest of Alaska Using Lidar Data

Tree height and canopy cover estimation presents significant challenges especially in vast and inaccessible areas where traditional ground-based methods prove impractical. Light Detection and Ranging (LiDAR) technology emerges as a transformative and efficient solution. This study focuses on a 1-hectare forest plot dominated by white spruce in the interior Alaska, monitored since 1993. The study aims to assess tree mortality and conditions by analyzing canopy cover changes and estimating height difference from 2017 to 2023, utilizing canopy height models inferred from LiDAR. The results, compared against 37 years of ground data, demonstrate substantial tree mortality. We validated the LiDAR-derived tree height by comparing it with the ground data and found that 84% of the trees exhibited height variations corresponding precisely with ground data tree canopy changes. If LiDAR data indicated an increase in height, ground data tree height also increased, and vice versa, showcasing LiDAR's potential in forest studies.